How to create a digital marketing strategy with AI

AI has opened up a vast array of new possibilities for digital marketers, enabling them to gain competitive advantages and engage with customers in innovative ways. Among such tools are artificial intelligence marketing platforms, which in essence, are software that leverage AI technology to perform tasks and help marketers make insightful business decisions.

From predicting customer behaviour to automating tasks, AI marketing tools can help marketers save time, increase efficiency, optimise efforts, and boost ROI by identifying patterns from historical marketing data. 

In this article, we’ll delve into the world of AI marketing strategy, exploring how to create a powerful digital marketing plan by harnessing AI-driven tools and technology.

The power of AI in marketing

Using AI for marketing has rapidly become a game-changer in the world of digital marketing. Its ability to process vast amounts of data, analyse patterns, and make real-time decisions has made it an indispensable tool for marketing teams. Here’s how you can harness the potential of AI to supercharge your marketing strategy:

1. Understand your target audience

One of the primary advantages of AI in marketing is its ability to analyse customer data comprehensively. With AI-driven tools, you can gain valuable insights into consumer behaviour, preferences, and buying patterns. This information is instrumental in creating highly targeted marketing campaigns that resonate with your audience.

2. Personalise content

AI technology enables you to personalise your content like never before. By analysing user data, AI can tailor marketing messages and product recommendations to individual customers. This personal touch not only enhances the customer experience but also boosts conversion rates.

3. Content generation

Creating engaging content is a fundamental aspect of any marketing strategy. AI can assist marketing teams by generating content ideas, optimising headlines, and even drafting articles. This streamlines the content creation process, allowing marketers to focus on strategy and creativity.

4. Real-time decision-making

In the fast-paced world of digital marketing, timing is crucial. AI can analyse data in real-time, helping you make informed decisions on when and where to run your marketing campaigns. This ensures that your efforts always align with the latest trends and consumer behaviour.

Now that we’ve explored the incredible potential of AI in marketing, let’s dive into the tools of AI for marketing strategy.

Seven best AI tools that help you to create a marketing strategy

Adzooma

Adzooma stands as a formidable AI marketing ally, offering an array of invaluable benefits for optimising your AI marketing strategy. With its AI-powered recommendations, it seamlessly identifies avenues for enhancing campaign performance. The platform’s automation capabilities extend to managing bid adjustments, fine-tuning keyword targeting, and conducting ad copy tests, significantly streamlining your campaign management tasks. By harnessing Adzooma, not only do you achieve time efficiency, but you also elevate the effectiveness of your AI marketing endeavours, yielding superior results.

Jasper

Jasper AI, formerly recognized as Conversion.ai, represents an advanced AI marketing solution tailored for crafting top-notch content across various channels, including social media, email, landing pages, and articles. It harnesses the capabilities of the GPT-3 model developed by OpenAI to transform text into engaging copy. Marketers can effortlessly generate compelling content by supplying input such as brand or product names. With this potent AI marketing tool, businesses spanning diverse sectors gain the capacity to craft more convincing, alluring content for their blog posts, landing pages, social media, advertisements, marketing emails, and beyond.

HubSpot

HubSpot’s AI tools empower marketers to unlock new levels of creativity and precision in their campaigns, ultimately driving enhanced engagement and results. Transform your marketing approach with HubSpot’s cutting-edge AI capabilities. Marketers now have access to powerful tools that facilitate content creation, campaign personalization, and data analysis, enabling the development of precise and influential marketing strategies. Additionally, it assists in crafting social media posts and generating images based on user prompts, ensuring that your marketing efforts are streamlined, efficient, and highly impactful. 

Murf AI

Murf stands as an AI voice synthesiser, ranking among the foremost AI marketing solutions. It empowers marketers to generate lifelike speech through natural language processing, eliminating the necessity for specialised hardware. This AI marketing solution streamlines marketers’ processes, facilitating the swift creation of audio and video content for various applications, such as social media, brand advertising, and marketing initiatives, all within a matter of minutes.

Adobe Sensei

Adobe’s commitment to innovation ensures that brands can leverage AI-driven marketing solutions to elevate their customer experiences, paving the way for enhanced engagement and loyalty. As a pioneering force in artificial intelligence, we recognise that generative AI holds the power to revolutionise all facets of marketing, spanning the realms of strategy, implementation, and analysis. With the introduction of Adobe Sensei GenAI, their latest AI technology, customer experience teams can harness the innate capabilities of generative AI. This empowers them to amplify their expertise and deliver customer journeys that are not only more precise but also deeply personalised. 

ClickUp

The introduction of ClickUp AI marks a transformative moment for marketing and sales units, offering enhanced efficiency, seamless collaboration, and expedited workflows. This innovative feature within ClickUp introduces AI capabilities that are poised to revolutionise the way teams operate. From streamlining processes and fostering creativity to facilitating project management and igniting innovative thinking, ClickUp AI is the catalyst for optimising productivity and content quality, all while ensuring consistency and scalability.

SurferSEO

SurferSEO is an AI-powered SEO tool that helps you optimise your content for search engines. It analyses the top-ranking pages for your target keywords and provides you with insights into their content structure, word count, and other factors. You can then use this information to create content that is more likely to rank well.

Final thoughts

As AI technology continues to evolve, its role in marketing will only become more prominent. By harnessing the power of AI-driven tools and technology, you can create a dynamic and data-driven marketing strategy that not only reaches your target audience but also engages them on a personalised level. Embrace the future of marketing with AI and elevate your brand to new heights.

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In recent years, we’ve seen a number of incredible AI applications in healthcare, manufacturing, finance, and beyond. So, why is it that up to 92% of AI projects still fail to yield business results? 

AI has significantly evolved over the last several decades, making it more challenging than ever for businesses to clearly understand and design AI successfully. Continue reading to learn more about top AI challenges shared by companies today—and how to solve them. 

Four solutions to top AI challenges 

Despite the complexity of AI, there are a number of ways companies can position themselves for success with machine learning models. Here are a few. 

1. Cultivate data and AI literacy

In the 2000s, companies were most focused on digital literacy (think: word processing and spreadsheets). In the 2010s, industries shifted their focus to data literacy—can we acquire the data and can we build models with that data? Today, AI literacy is top-of-mind.

According to Harvard Business Review, fewer than 25% of the workforce would consider themselves data literate. Defined as the ability to assess, understand, and utilize data, data literacy is a skill that directly enables individuals to work with tools like machine learning models.

Cultivating data and AI literacy within your organization, through educational workshops or insightful articles, will significantly improve AI adoption rates and employee trust in AI-based initiatives.  

2. Clearly define your business value

With AI, the path to defining and deriving business value is often unclear. Oftentimes, companies will have the right data, design an adequate model, and identify the level of accuracy the model can achieve, but the team does not consider the actual human or group of humans that will be making decisions based on the model. This is one area where we see a high failure rate.

When developing your AI strategy, be sure to account for how the AI’s recommendations will be interpreted and used by your team. Will your team need a dashboard explaining the results? How else can you ensure your team trusts and accurately uses the information? 

3. Understand the journey to AI is iterative

AI strategy and design can often be broken down into two processes:

1. Design. Where you are working to build a statistically valid model that can solve your problem. This process often requires experimentation with data and redefined requirements based on revealed constraints. 
2. Develop. Where you are developing the solution and translating it into the hands of the end user(s). 

One of the most important phases of AI design is building resilience. You will likely encounter instances where data in the real world doesn’t match the training data used to build the model. Or, you may realize decision makers or other end users don’t trust the model enough to use it. Working through these challenges to design a resilient, trustworthy model will result in higher success rates compared to companies that ignore the complexity of the AI process. 

4. Mitigate unintended bias and risk

Risk mitigation and bias prevention must be at the forefront of your AI strategy in order to truly generate business value with AI. Involve diverse humans in your feedback loop, test your AI against unexpected situations, and understand the costs of undetected bias in your solution. 

Reducing the chance of negative bias in your solution protects end users from harm, and cultivates a deeper level of trust between your organization, your solution, and stakeholders. 

Improve your AI literacy with Trusted AI insights  

Improving your AI literacy—educating yourself and your team—is key to successfully strategizing and designing trustworthy AI. To stay up-to-date on AI news and gather more insights from data scientists, subscribe to Pandata’s monthly email digest: The Voices of Trusted AI.

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Last year, the European Commission drafted AI laws. While the US and China are set to dominate AI development with their vast resources, economic might, and light-touch regulation, European rivals – including the UK and EU members – believe they can lead in ethical standards.

In the draft of the EU regulations, companies that are found guilty of AI misuse face a fine of €30 million or six percent of their global turnover (whichever is greater). The risk of such fines has been criticised as driving investments away from Europe.

The EU’s draft AI regulation classifies systems into three risk categories:

• Limited risk – includes systems like chatbots, inventory management, spam filters, and video games.
• High risk – includes systems that make vital decisions like evaluating creditworthiness, recruitment, justice administration, and biometric identification in non-public spaces.
• Unacceptable risk – includes systems that are manipulative or exploitative, create social scoring, or conduct real-time biometric authentication in public spaces for law enforcement.

Unacceptable risk systems will face a blanket ban from deployment in the EU while limited risk will require minimal oversight.

Organisations deploying high-risk AI systems would be required to have things like:

• Human oversight.
• A risk-management system.
• Record keeping and logging.
• Transparency to users.
• Data governance and management.
• Conformity assessment.
• Government registration.

However, the cumbersome nature of the EU – requiring agreement from all member states, each with their own priorities – means that new regulations are often subject to more debate and delay than national lawmaking.

Reuters reports that two key lawmakers on Wednesday said the EU’s AI regulations will likely take over a year more to agree. The delay is primarily due to debates over whether facial recognition should be banned and who should enforce the rules.

“Facial recognition is going to be the biggest ideological discussion between the right and left,” said one lawmaker, Dragos Tudorache, in a Reuters interview.

“I don’t believe in an outright ban. For me, the solution is to put the right rules in place.”

With leading academic institutions and more than 1,300 AI companies employing over 30,000 people, the UK is the biggest destination for AI investment in Europe and the third in the world. Between January and June 2021, global investors poured £13.5 billion into more than 1,400 “deep tech” UK private technology firms—more than Germany, France, and Israel combined.

In September 2021, the UK published its 10-year National Artificial Intelligence Strategy in a bid to secure its European AI leadership. Governance plays a large role in the strategy.

“The UK already punches above its weight internationally and we are ranked third in the world behind the USA and China in the list of top countries for AI,” commented DCMS Minister Chris Philp.

“We’re laying the foundations for the next ten years’ growth with a strategy to help us seize the potential of artificial intelligence and play a leading role in shaping the way the world governs it.”

As part of its strategy, the UK is creating an ‘AI Standards Hub’ to coordinate the country’s engagement in establishing global rules and is working with The Alan Turing Institute to update guidance on AI ethics and safety.

“We are proud of creating a dynamic, collaborative community of diverse researchers and are growing world-leading capabilities in responsible, safe, ethical, and inclusive AI research and innovation,” said Professor Sir Adrian Smith, Chief Executive of The Alan Turing Institute.

Striking a balance between innovation-stifling overregulation and ethics-compromising underregulation is never a simple task. It will be interesting to observe how AI regulations in Europe will differ across the continent and beyond.

(Photo by Christian Lue on Unsplash)

Related: British intelligence agency GCHQ publishes ‘Ethics of AI’ report

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